class_docstr: Hand-coded policy that returns random actions.
class_docstr_tok: ["Hand-coded","policy","that","returns","random","actions","."]
code: def compute_log_likelihoods(
      self,
      actions,
      obs_batch,
      state_batches=None,
      prev_action_batch=None,
      prev_reward_batch=None,
      **kwargs,
  ):
      return np.array([random.random()] * len(obs_batch))
  
file_path: ray/python/ray/rllib/examples/policy/random_policy.py
imports: from gymnasium.spaces import Box
  import numpy
  import random
  import tree
  from typing import List, Optional, Union
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.typing import ModelWeights, TensorStructType, TensorType
label_desc: Use image node_id 5 for calling the RandomPolicy obj's underlying member method code with example usage: obj.compute_log_likelihoods(actions, obs_batch, state_batches, prev_action_batch, prev_reward_batch) and returns: np